Enabling secure video sharing by exploiting data sparsity

ABSTRACT

In one example, the present disclosure describes a device, computer-readable medium, and method for enabling secure video sharing by exploiting data sparsity. In one example, the method includes applying a transformation to a video dataset containing a plurality of video samples, to produce a plurality of sparse vectors in a first dimensional space, wherein each sparse vector of the plurality of sparse vectors corresponds to one video sample of the plurality of video samples, and multiplying each sparse vector of the plurality of sparse vectors by a transformation matrix to produce a plurality of reduced vectors in a second dimensional space, wherein the dimension of the second dimensional space is smaller than a dimension of the first dimensional space, and wherein the plurality of reduced vectors in the second dimensional space hides information about the video dataset while preserving relational properties between the plurality of video samples.

The present disclosure relates generally to data security, and relatesmore particularly to devices, non-transitory computer-readable media,and methods for enabling secure video sharing by exploiting datasparsity.

BACKGROUND

Data-powered machine learning applications and services have provenuseful in various fields including medicine, retail, financial services,the automotive industry, and others. For instance, machine learning canhelp a business detect patterns, market trends, and customer preferencesin large, complex data sets in a more accurate and more efficient mannerthan would be possible for a human analyst.

SUMMARY

In one example, the present disclosure describes a device,computer-readable medium, and method for enabling secure video sharingby exploiting data sparsity. In one example, the method includesapplying a transformation to a video dataset containing a plurality ofvideo samples, to produce a plurality of sparse vectors in a firstdimensional space, wherein each sparse vector of the plurality of sparsevectors corresponds to one video sample of the plurality of videosamples, and multiplying each sparse vector of the plurality of sparsevectors by a transformation matrix to produce a plurality of reducedvectors in a second dimensional space, wherein the dimension of thesecond dimensional space is smaller than a dimension of the firstdimensional space, and wherein the plurality of reduced vectors in thesecond dimensional space hides information about the video dataset whilepreserving relational properties between the plurality of video samples.

In another example, a device includes a processor and a non-transitorycomputer-readable medium storing instructions which, when executed bythe processor, cause the processor to perform operations. The operationsinclude applying a transformation to a video dataset containing aplurality of video samples, to produce a plurality of sparse vectors ina first dimensional space, wherein each sparse vector of the pluralityof sparse vectors corresponds to one video sample of the plurality ofvideo samples, and multiplying each sparse vector of the plurality ofsparse vectors by a transformation matrix to produce a plurality ofreduced vectors in a second dimensional space, wherein the dimension ofthe second dimensional space is smaller than a dimension of the firstdimensional space, and wherein the plurality of reduced vectors in thesecond dimensional space hides information about the video dataset whilepreserving relational properties between the plurality of video samples.

In another example, a non-transitory computer-readable medium storesinstructions which, when executed by a processor, cause the processor toperform operations. The operations include applying a transformation toa video dataset containing a plurality of video samples, to produce aplurality of sparse vectors in a first dimensional space, wherein eachsparse vector of the plurality of sparse vectors corresponds to onevideo sample of the plurality of video samples, and multiplying eachsparse vector of the plurality of sparse vectors by a transformationmatrix to produce a plurality of reduced vectors in a second dimensionalspace, wherein the dimension of the second dimensional space is smallerthan a dimension of the first dimensional space, and wherein theplurality of reduced vectors in the second dimensional space hidesinformation about the video dataset while preserving relationalproperties between the plurality of video samples.

BRIEF DESCRIPTION OF THE DRAWINGS

The teachings of the present disclosure can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example network related to the present disclosure;

FIG. 2 illustrates a flowchart of an example method for enabling securevideo sharing;

FIG. 3 illustrates example vectors representing an example video datasetthat may be transformed in accordance with the method of FIG. 2 toprotect data contained therein and

FIG. 4 depicts a high-level block diagram of a computing devicespecifically programmed to perform the functions described herein.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION

In one example, the present disclosure describes a method, apparatus,and non-transitory computer readable storage medium for enabling securevideo sharing by exploiting data sparsity. As discussed above,data-powered machine learning applications and services have provenuseful in various fields including medicine, retail, financial services,the automotive industry, and others. These machine learning applicationsand services rely on the sharing of digital data. With mobile technologymaking it easier for individuals to access and share data anytime andanywhere, video data in particular is emerging as one of the mostpopular, if not the most popular, types of data being shared.

Examples of the present disclosure use random projections to preservethe security of video data. The video data may be shared for thepurposes of building machine learning applications. In particular,examples of the present disclosure exploit the concept of data sparsityin video data. Digital video, unlike many other types of digital data,is generally sparse in wavelet transform, Fourier transform, or discretecosine transform (DCT) bases. Sparse, N-dimensional vectorized videodata (x) can be transformed into a (relatively) low, M-dimensionalvector (y) by multiplying the N-dimensional vector by a random M×Nmatrix. In this case, M<N, i.e., the M-dimensional space is of a lowerdimension than the N-dimensional space. It is very difficult to inferdetails about the N-dimensional vector from the M-dimensional vector;however, the N-dimensional vector can be recovered from theM-dimensional vector with knowledge of the M×N matrix. Moreover, newdata obtained in the M-dimensional space can be reliably visualized backin the N-dimensional space.

To better understand the present disclosure, FIG. 1 illustrates anexample network 100, related to the present disclosure. The network 100may be any type of communications network, such as for example, atraditional circuit switched network (CS) (e.g., a public switchedtelephone network (PSTN)) or an Internet Protocol (IP) network (e.g., anIP Multimedia Subsystem (IMS) network, an asynchronous transfer mode(ATM) network, a wireless network, a cellular network (e.g., 2G, 3G andthe like), a long term evolution (LTE) network, and the like) related tothe current disclosure. It should be noted that an IP network is broadlydefined as a network that uses Internet Protocol to exchange datapackets. Additional exemplary IP networks include Voice over IP (VoIP)networks, Service over IP (SoIP) networks, and the like.

In one example, the network 100 may comprise a core network 102. In oneexample, core network 102 may combine core network components of acellular network with components of a triple play service network; wheretriple play services include telephone services, Internet services, andtelevision services to subscribers. For example, core network 102 mayfunctionally comprise a fixed mobile convergence (FMC) network, e.g., anIP Multimedia Subsystem (IMS) network. In addition, core network 102 mayfunctionally comprise a telephony network, e.g., an InternetProtocol/Multi-Protocol Label Switching (IP/MPLS) backbone networkutilizing Session Initiation Protocol (SIP) for circuit-switched andVoice over Internet Protocol (VoIP) telephony services. Core network 102may also further comprise an Internet Service Provider (ISP) network. Inone embodiment, the core network 102 may include an application server(AS) 104 and a database (DB) 106. Although only a single AS 104 and asingle DB 106 are illustrated, it should be noted that any number ofapplication servers 104 or databases 106 may be deployed. Furthermore,for ease of illustration, various additional elements of core network102 are omitted from FIG. 1.

In one embodiment, the AS 104 may comprise a computing devicespecifically programmed to perform the functions described herein, asillustrated in FIG. 4 and discussed below. In one embodiment, the AS 104may perform the methods discussed below related to enabling secure videosharing. For instance, the AS 104 may transform a video dataset into oneor more sparse vectors in a first dimensional space. The AS 104 mayfurther transform the sparse vectors in the first dimensional space intoone or more vectors in a second dimensional space (which may be of alower dimension than the first dimensional space). In a further examplestill, the AS 104 may construct and/or train machine learning modelsusing video data that has been transformed for secure sharing.

In one example, the DB 106 may store video data sets. The DB 106 mayalso store vectors representing samples contained in the video datasets. For instance, the DB 106 may store the vectors in the first and/orsecond dimensional spaces discussed above. In addition, the DB 106 maystore one or more transformation matrices for use (e.g., by the AS 104)in transforming vectors from the first dimensional space to the seconddimensional space and/or vice versa.

The core network 102 may be in communication with one or more wirelessaccess networks 120 and 122. Either or both of the access networks 120and 122 may include a radio access network implementing suchtechnologies as: global system for mobile communication (GSM), e.g., abase station subsystem (BSS), or IS-95, a universal mobiletelecommunications system (UMTS) network employing wideband codedivision multiple access (WCDMA), or a CDMA3000 network, among others.In other words, either or both of the access networks 120 and 122 maycomprise an access network in accordance with any “second generation”(2G), “third generation” (3G), “fourth generation” (4G), Long TermEvolution (LTE), or any other yet to be developed futurewireless/cellular network technology including “fifth generation” (5G)and further generations. The operator of core network 102 may provide adata service to subscribers via access networks 120 and 122. In oneembodiment, the access networks 120 and 122 may all be different typesof access networks, may all be the same type of access network, or someaccess networks may be the same type of access network and other may bedifferent types of access networks. The core network 102 and the accessnetworks 120 and 122 may be operated by different service providers, thesame service provider or a combination thereof.

In one example, the access network 120 may be in communication with oneor more user endpoint devices (also referred to as “endpoint devices” or“UE”) 108 and 110, while the access network 122 may be in communicationwith one or more user endpoint devices 112 and 114. Access networks 120and 122 may transmit and receive communications between respective UEs108, 110, 112, and 114 and core network 102 relating to communicationswith web servers, AS 104, and/or other servers via the Internet and/orother networks, and so forth.

In one embodiment, the user endpoint devices 108, 110, 112, and 114 maybe any type of subscriber/customer endpoint device configured for wiredand/or wireless communication such as a desktop computer, a laptopcomputer, a Wi-Fi device, a Personal Digital Assistant (PDA), a mobilephone, a smartphone, an email device, a computing tablet, a messagingdevice, a wearable “smart” device (e.g., a smart watch or fitnesstracker), a portable media device (e.g., an MP3 player), a gamingconsole, a portable gaming device, a set top box (STB), and the like. Inone example, any one or more of the user endpoint devices 108, 110, 112,and 114 may have both cellular and non-cellular access capabilities andmay further have wired communication and networking capabilities. Itshould be noted that although only four user endpoint devices areillustrated in FIG. 1, any number of user endpoint devices may bedeployed.

It should also be noted that as used herein, the terms “configure” and“reconfigure” may refer to programming or loading a computing devicewith computer-readable/computer-executable instructions, code, and/orprograms, e.g., in a memory, which when executed by a processor of thecomputing device, may cause the computing device to perform variousfunctions. Such terms may also encompass providing variables, datavalues, tables, objects, or other data structures or the like which maycause a computer device executing computer-readable instructions, code,and/or programs to function differently depending upon the values of thevariables or other data structures that are provided. For example, anyone or more of the user endpoint devices 108, 110, 112, and 114 may hostan operating system for presenting a user interface that may be used tosend data to the AS 104 (e.g., video datasets for sharing, requests fordata, requests for machine learning models, etc.) and for reviewing datasent by the AS 104 (e.g., results of machine learning models, videodatasets, etc.).

Those skilled in the art will realize that the network 100 has beensimplified. For example, the network 100 may include other networkelements (not shown) such as border elements, routers, switches, policyservers, security devices, a content distribution network (CDN) and thelike. The network 100 may also be expanded by including additionalendpoint devices, access networks, network elements, applicationservers, etc. without altering the scope of the present disclosure.

To further aid in understanding the present disclosure, FIG. 2illustrates a flowchart of an example method 200 for enabling securevideo sharing. In one example, the method 200 may be performed by anapplication server, e.g., AS 104 illustrated in FIG. 1 or the computingdevice 400 illustrated in FIG. 4. However, any references in thediscussion of the method 200 to the AS 104 of FIG. 1 or the computingdevice 400 of FIG. 4 are not intended to limit the means by which themethod 200 may be performed. For illustrative purposes, the method 200is described in greater detail below in connection with an exampleperformed by a processing system.

FIG. 2 may be referred to in conjunction with FIG. 3, which illustratesexample vectors 300 and 304 representing an example video dataset thatmay be transformed in accordance with the method 200 of FIG. 2 toprotect data contained therein.

The method 200 begins in step 202. In step 204, the processing systemmay acquire a N-dimensional video dataset, D. The video dataset maycontain a plurality of digital videos for a given video processing task.The plurality of digital videos may correspond to any video application.In one example, the video dataset D includes labels, L (e.g., forsupervised learning). In another example, however, the video dataset Dincludes no labels.

In step 206, the processing system may apply a transformation, T, to thevideo dataset D. Application of the transformation T to the videodataset D produces one sparse N-dimensional vector, x, for each sample(i.e., individual video) in the video dataset D. Thus, as a result ofstep 206, a plurality of N-dimensional vectors x will be produced (e.g.,one N-dimensional vector x for each sample in the video dataset D). FIG.3, for instance, illustrates an example N-dimensional vector 300 thatmay be produced in accordance with step 206 of the method 200. In theexample illustrated, shaded blocks of the N-dimensional vector 300represent non-zero pixel level values for corresponding pixels of asample, while unshaded blocks of the N-dimensional vector 300 representzero pixel level values. The pixel level values describe how brightand/or what color the corresponding pixels should be. In one example,the transformation T may comprise a wavelet transform, a Fouriertransform, or a discrete cosine transform.

In step 208, the processing system may multiply each N-dimensionalvector x by a random M×N transformation matrix, φ, where M<N. FIG. 3,for instance, illustrates an example M×N transformation matrix 302. Asillustrated, M<N (e.g., in this example, M=8 and N=16, but N does notnecessarily have to be twice as big as N).

Multiplication of an N-dimensional vector x by the transformation matrixφ produces a smaller (or “reduced”) M-dimensional vector, y. FIG. 3, forinstance illustrates an example M-dimensional vector 304 that may beproduced by multiplying the example N-dimensional vector 300 by the M×Ntransformation matrix 302. Thus, as a result of step 208, a plurality ofM-dimensional vectors y (similar to the M-dimensional vector 304 of FIG.3) will be produced (e.g., one M-dimensional vector y for eachx-dimensional vector in the original video dataset D). The resultantplurality of M-dimensional vectors y may be collectively referred to asD*. D* also represents the secure data domain in which the originalinformation about the N-dimensional video dataset D is hidden. However,the relational properties of the N-dimensional video dataset D arepreserved in the secure domain D*.

Each M-dimensional vector y produced in step 208 preserves pairwisedistances in the original video dataset D. For instance, the distanceratios between three example original videos (or samples) x₁, x₂, and x₃would be equal to the distance ratios between three example transformedvideos y₁, y₂, and y₃ corresponding to the example original videos x₁,x₂, and x₃.

Moreover, little trace of the original video dataset D can be found inthe plurality of M-dimensional vectors D*. That is, it is very difficultto infer details about the plurality of N-dimensional vectors x from theplurality of M-dimensional vectors y. Thus, the plurality ofM-dimensional vectors D* securely protects the data contained in theoriginal video dataset D.

The original video dataset D can be reconstructed from the plurality ofM-dimensional vectors D*, but only with knowledge of the transformationmatrix cp. With knowledge of the transformation matrix φ, one can derivethe plurality of N-dimensional vectors x from the correspondingplurality of M-dimensional vectors y.

The processing system may store the plurality of M-dimensional vectorsy, e.g., in the DB 106 of FIG. 1, in step 210.

The method may end in step 212.

The plurality of M-dimensional vectors D* produced by the method 200 maybe provided as inputs to a machine learning technique that seeks tobuild a machine learning model R for detecting patterns, market trends,customer preferences, and/or other data relationships. If labels L areavailable with the original video dataset D, then the plurality ofM-dimensional vectors D* may be provided as inputs to a supervisedmachine learning technique. However, if labels are not available withthe original video dataset D, then the plurality of M-dimensionalvectors D* may be provided as inputs to an unsupervised machine learningtechnique. The utility of the plurality of M-dimensional vectors D* isnot limited to a specific type of machine learning technique.

Moreover, accuracy metrics for the machine learning model R may besecurely computed using the learned data representations. Theperformance of accuracy metrics obtained using the learned datarepresentations of the machine learning model R will be as accurate asany accuracy metrics that may be obtained using the original videodataset D, but will also be more secure. Moreover, new information(e.g., new vectors) obtained in the M-dimensional space (e.g., obtainedby building machine learning models using a plurality of data sources)can be reliably visualized in the N-dimensional space (which wouldfacilitate better interpretability). However, as noted above, theresults of the machine learning model R cannot be transformed back intothe original N-dimensional space without knowledge of the transformationmatrix φ (e.g., and using inverse mapping).

The ability to reduce the dimension of a video dataset (e.g., from theN-dimensional space to the M-dimensional space) may have utilitiesbeyond data security as well. For instance, reducing the dimension ofthe video dataset may minimize the amount of space required to store thevideo dataset. The savings in storage space may reach exponentialproportions in the machine learning context, where a machine learningmodel could be trained using as many as billions of video datasets.

The disclosed approach for enabling secure video sharing may also proveuseful for crowd sourcing. For instance, as the need for video datagrows, individuals and organizations may find it beneficial to sharedata with others. The disclosed approach enables powerful data models tobe built without directly revealing the original data used to train thedata models. Thus, video security can be seamlessly merged with crowdsourced data models.

Although not expressly specified above, one or more steps of the method200 may include a storing, displaying and/or outputting step as requiredfor a particular application. In other words, any data, records, fields,and/or intermediate results discussed in the method can be stored,displayed and/or outputted to another device as required for aparticular application. Furthermore, operations, steps, or blocks inFIG. 2 that recite a determining operation or involve a decision do notnecessarily require that both branches of the determining operation bepracticed. In other words, one of the branches of the determiningoperation can be deemed as an optional step. Furthermore, operations,steps or blocks of the above described method(s) can be combined,separated, and/or performed in a different order from that describedabove, without departing from the examples of the present disclosure.

FIG. 4 depicts a high-level block diagram of a computing devicespecifically programmed to perform the functions described herein. Forexample, any one or more components or devices illustrated in FIG. 1 ordescribed in connection with the method 200 may be implemented as thesystem 400. For instance, an application server could be implemented asillustrated in FIG. 4.

As depicted in FIG. 4, the computing device 400 comprises a hardwareprocessor element 402, a memory 404, a module 405 for enabling securevideo sharing, and various input/output (I/O) devices 406.

The hardware processor 402 may comprise, for example, a microprocessor,a central processing unit (CPU), or the like. The memory 404 maycomprise, for example, random access memory (RAM), read only memory(ROM), a disk drive, an optical drive, a magnetic drive, and/or aUniversal Serial Bus (USB) drive. The module 405 for enabling securevideo sharing may include circuitry and/or logic for performing specialpurpose functions relating to the tuning individual word weights usedfor sentiment analysis techniques. The input/output devices 406 mayinclude, for example, a camera, a video camera, storage devices(including but not limited to, a tape drive, a floppy drive, a hard diskdrive or a compact disk drive), a receiver, a transmitter, a speaker, amicrophone, a transducer, a display, a speech synthesizer, a hapticdevice, an output port, or a user input device (such as a keyboard, akeypad, a mouse, and the like).

Although only one processor element is shown, it should be noted thatthe computing device may employ a plurality of processor elements.Furthermore, although only one computing device is shown in the Figure,if the method(s) as discussed above is implemented in a distributed orparallel manner for a particular illustrative example, i.e., the stepsof the above method(s) or the entire method(s) are implemented acrossmultiple or parallel computing devices, then the computing device ofthis Figure is intended to represent each of those multiple computingdevices. Furthermore, one or more hardware processors can be utilized insupporting a virtualized or shared computing environment. Thevirtualized computing environment may support one or more virtualmachines representing computers, servers, or other computing devices. Insuch virtualized virtual machines, hardware components such as hardwareprocessors and computer- readable storage devices may be virtualized orlogically represented.

It should be noted that the present disclosure can be implemented insoftware and/or in a combination of software and hardware, e.g., usingapplication specific integrated circuits (ASIC), a programmable logicarray (PLA), including a field-programmable gate array (FPGA), or astate machine deployed on a hardware device, a general purpose computeror any other hardware equivalents, e.g., computer readable instructionspertaining to the method(s) discussed above can be used to configure ahardware processor to perform the steps, functions and/or operations ofthe above disclosed method(s). In one example, instructions and data forthe present module or process 405 for enabling secure video sharing(e.g., a software program comprising computer-executable instructions)can be loaded into memory 404 and executed by hardware processor element402 to implement the steps, functions or operations as discussed abovein connection with the example methods 200 or 300. Furthermore, when ahardware processor executes instructions to perform “operations,” thiscould include the hardware processor performing the operations directlyand/or facilitating, directing, or cooperating with another hardwaredevice or component (e.g., a co-processor and the like) to perform theoperations.

The processor executing the computer readable or software instructionsrelating to the above described method(s) can be perceived as aprogrammed processor or a specialized processor. As such, the presentmodule 405 for enabling secure video sharing (including associated datastructures) of the present disclosure can be stored on a tangible orphysical (broadly non-transitory) computer-readable storage device ormedium, e.g., volatile memory, non-volatile memory, ROM memory, RAMmemory, magnetic or optical drive, device or diskette and the like. Morespecifically, the computer-readable storage device may comprise anyphysical devices that provide the ability to store information such asdata and/or instructions to be accessed by a processor or a computingdevice such as a computer or an application server.

While various examples have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of the disclosure should not belimited by any of the above-described example examples, but should bedefined only in accordance with the following claims and theirequivalents.

What is claimed is:
 1. A method, comprising: applying a transformationto a video dataset containing a plurality of video samples, to produce aplurality of sparse vectors in a first dimensional space, wherein eachsparse vector of the plurality of sparse vectors corresponds to onevideo sample of the plurality of video samples; and multiplying eachsparse vector of the plurality of sparse vectors by a transformationmatrix to produce a plurality of reduced vectors in a second dimensionalspace, wherein the dimension of the second dimensional space is smallerthan a dimension of the first dimensional space, and wherein theplurality of reduced vectors in the second dimensional space hidesinformation about the video dataset while preserving relationalproperties between the plurality of video samples.
 2. The method ofclaim 1, wherein the transformation comprises a wavelet transform. 3.The method of claim 1, wherein the transformation comprises a Fouriertransform.
 4. The method of claim 1, wherein the transformationcomprises a discrete cosine transform.
 5. The method of claim 1, whereinthe video dataset is labeled.
 6. The method of claim 1, wherein thetransformation matrix is an M×N matrix, wherein N is the dimension ofthe first dimensional space, and wherein M is the dimension of thesecond dimensional space.
 7. The method of claim 6, wherein the M×Nmatrix is a random matrix.
 8. The method of claim 1, wherein therelational properties comprise pairwise distances.
 9. The method ofclaim 8, wherein distance ratios between pairs of the plurality ofsparse vectors are equal to distance ratios between pairs of theplurality of reduced vectors corresponding to the pairs of the pluralityof sparse vectors.
 10. The method of claim 1, wherein the transformationmatrix allows transformation of the plurality of reduced vectors backinto the video dataset.
 11. The method of claim 1, further comprising:providing the plurality of reduced vectors as inputs to a machinelearning technique that trains a machine learning model.
 12. The methodof claim 11, further comprising: acquiring a new vector in the seconddimensional space through operation of the machine learning model; andtransforming the new vector, using the transformation matrix, into thefirst dimensional space.
 13. A device, comprising: a processor; and anon-transitory computer-readable medium storing instructions which, whenexecuted by the processor, cause the processor to perform operationscomprising: applying a transformation to a video dataset containing aplurality of video samples, to produce a plurality of sparse vectors ina first dimensional space, wherein each sparse vector of the pluralityof sparse vectors corresponds to one video sample of the plurality ofvideo samples; and multiplying each sparse vector of the plurality ofsparse vectors by a transformation matrix to produce a plurality ofreduced vectors in a second dimensional space, wherein the dimension ofthe second dimensional space is smaller than a dimension of the firstdimensional space, and wherein the plurality of reduced vectors in thesecond dimensional space hides information about the video dataset whilepreserving pairwise distances between the plurality of video samples.14. A non-transitory computer-readable medium storing instructionswhich, when executed by a processor, cause the processor to performoperations comprising: applying a transformation to a video datasetcontaining a plurality of video samples, to produce a plurality ofsparse vectors in a first dimensional space, wherein each sparse vectorof the plurality of sparse vectors corresponds to one video sample ofthe plurality of video samples; and multiplying each sparse vector ofthe plurality of sparse vectors by a transformation matrix to produce aplurality of reduced vectors in a second dimensional space, wherein thedimension of the second dimensional space is smaller than a dimension ofthe first dimensional space, and wherein the plurality of reducedvectors in the second dimensional space hides information about thevideo dataset while preserving pairwise distances between the pluralityof video samples.
 15. The non-transitory computer-readable medium ofclaim 14, wherein the transformation matrix is an M×N matrix, wherein Nis the dimension of the first dimensional space, and wherein M is thedimension of the second dimensional space.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the M×N matrix is a randommatrix.
 17. The non-transitory computer-readable medium of claim 14,wherein the relational properties comprise pairwise distances.
 18. Thenon-transitory computer-readable medium of claim 17, wherein distanceratios between pairs of the plurality of sparse vectors are equal todistance ratios between pairs of the plurality of reduced vectorscorresponding to the pairs of the plurality of sparse vectors.
 19. Thenon-transitory computer-readable medium of claim 14, wherein thetransformation matrix allows transformation of the plurality of reducedvectors back into the video dataset.
 20. The non-transitorycomputer-readable medium of claim 14, wherein the operations furthercomprise: providing the plurality of reduced vectors as inputs to amachine learning technique that trains a machine learning model;acquiring a new vector in the second dimensional space through operationof the machine learning model; and transforming the new vector, usingthe transformation matrix, into the first dimensional space.